Data Mining and Unsupervised Machine Learning in Canadian In Situ Oil Sands Database for Knowledge Discovery and Carbon Cost Analysis

A better understanding of greenhouse gas (GHG) emissions resulting from oil sands (bitumen) extraction can help to meet global oil demands, identify potential mitigation measures, and design effective carbon policies. While several studies have attempted to model GHG emissions from oil sands extractions, these studies have encountered data availability challenges, particularly with respect to actual fuel use data, and have thus struggled to accurately quantify GHG emissions. This dataset contains actual operational data from 20 in-situ oil sands operations, including information for fuel gas, flare gas, vented gas, production, steam injection, gas injection, condensate injection, and C3 injection.深入了解油砂（沥青砂）开采产生的温室气体（GHG）排放，有助于满足全球石油需求、识别潜在减排措施并制定有效的碳政策。尽管已有多项研究尝试对油砂开采产生的温室气体排放进行建模，但这些研究均面临数据可用性难题，尤其是在实际燃料使用数据方面，因此难以准确量化温室气体排放量。本数据集包含20项原位油砂开采作业的实际运营数据，涵盖燃料气、火炬气、放空气、生产数据、蒸汽注入、气体注入、凝析油注入以及C3注入等相关信息。